The role of electric interactions in the retention index concept; Implications in quantitative structure-retention studies

Exploring the chemical subspace of RPLC: A data driven approach

BACKGROUND

The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC.

RESULTS

We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace.

SIGNIFICANCE AND NOVELTY

The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

Qualitative analysis of flavors and fragrances added to tea by using GC-MS

A precise identification method was developed to identify the flavors and fragrances added to tea matrix artificially using gas chromatography with mass spectrometry and gas chromatography with quadrupole time-of-flight mass spectrometry. The proposed method was based on the corresponding "three-column retention indices, two exact mass numbers, one mass spectrum matching degree" database of 40 kinds of common flavors and fragrances. The intraday and the interday relative standard deviation of the retention indices were less than 0.048 and 0.093%, respectively. The accuracy of exact mass was between 0.15 and 6.22 ppm. And the validation of the created database was performed by analyzing the tea samples. Thus, the proposed method is suitable for the precise identification of the flavors and fragrances added to tea matrix artificially without standard substances as a reference.

Development of comprehensive descriptors for multiple linear regression and artificial neural network modeling of retention behaviors of a variety of compounds on different stationary phases

A new series of six comprehensive descriptors that represent different features of the gas-liquid partition coefficient, K(L), for commonly used stationary phases is developed. These descriptors can be considered as counterparts of the parameters in the Abraham solvatochromic model of solution. A separate multiple linear regression (MLR) model was developed by using the six descriptors for each stationary phase of poly(ethylene glycol adipate) (EGAD), N,N,N',N'-tetrakis(2-hydroxypropyl) ethylenediamine (THPED), poly(ethylene glycol) (Ucon 50 HB 660) (U50HB), di(2-ethylhexyl)phosphoric acid (DEHPA) and tetra-n-butylammonium N,N-(bis-2-hydroxylethyl)-2-aminoethanesulfonate (QBES). The results obtained using these models are in good agreement with the experiment and with the results of the empirical model based on the solvatochromic theory. A 6-6-5 neural network was developed using the descriptors appearing in the MLR models as inputs. Comparison of the mean square errors (MSEs) shows the superiority of the artificial neural network (ANN) over that of the MLR. This indicates that the retention behavior of the molecules on different columns show some nonlinear characteristics. The experimental solvatochromic parameters proposed by Abraham can be replaced by the calculated descriptors in this work.

Retention and separation studies of cholesterol and bile acids using thermostated thin-layer chromatography

The influence of temperature on retention and separation of cholesterol and bile acids, using reversed-phase thin-layer chromatography, was studied. As mobile phases methanol-water mixtures of various compositions were used. Chromatographic experiments were performed using vapor-saturated chambers at temperatures ranging from 5 to 60 degrees C. A linear relationship between R(M) values and temperature (1/T) as well as mobile phase composition was observed. The elution order of steroids under the conditions investigated was discussed. Each chromatogram was evaluated using simple optimization parameters and the best chromatographic conditions for the separation of multicomponent samples were chosen.